For the given input audio file (.wav) the program will give the real time audio spectrum by performing Fast Fourier Transform (FFT) on the audio samples and plot the graph using OpenGL. This Project is implemented in C++ using OpenGL. Three frameworks Aquila-dsp, Kiss-Fft, and SFML are used to perform audio sampling, fast fourier transform and audio playback respectively. The development and testing of this project is done on Linux (ubuntu) using 4.2.0-16-generic Kernel. Interface for the program is given with the help of keyboard.
Frameworks / APIs used
- The following c++ frameworks are used in this project. 2 Simple and Fast Multimedia Library (SFML)
The development of the project was done with these requirements. The can support lower versions also. It has not been tested.
- C++ compiler : g++ with c++11 support (-std=gnu++11) (version >= 5.2.1)
- OpenGL (version >=v4.5)
- Aquila-dsp framework (version >=v3.0)
- Kiss-fft Framework (version >=v1.3.0)
- SFML framework >=2.3.2
- Git >=v2.5.0
Use `Left/Right` to move horizontally. Use `Up/Down` to change the horizontal scale. Use `Home` button to reset the position and scale. Press ` F7 ` to toggle interpolation. Press `F8` to toggle clamping. Press `F9` to toggle drawing points. Press `q` to exit the program. Press `p` to to toggle audio play/pause. Press `r` to reload audio and play from the beginning. Press `right arrow` to seek audio forward by 5 seconds and move +ve x axis. Press `left arrow` to seek audio backward by 5 seconds and move -ve x axis. Press `down arrow` to decrease scale/1.5. Press `up arrow` to increase scale*1.5.
Step 1: Begin Int framePointer = 0, N = 32768. Step 2: Load input audio file and play it using the audio Library Step 3: For i = framePointer to → framePointer + N < total_samples_count Collect N samples from the audio file Step 4: Apply suitable window function (e.g. Hann aka Hanning window) Step 5: Apply Fast Fourier transform (FFT) on the array elements and collect N/2 Complex numbers having real part and imaginary part. (NB: if using typical complex-to-complex FFT then set imaginary parts of input array to zero) Step 6: Calculate the magnitude of N/2FFT data. magnitude = sqrt(re * re +img *img) Step 7: Convert magnitude to dB (log)scale. (optional) 20 * log10(magnitude) Step 8: Plot N/2 log(magnitude)values. Step 9: If N >= total_samples_count Exit. Else goto Step 3. Step 10: End. ## Flow Chart
Compiling the frameworks
CMake: Aquila relies on CMake as a build tool, so make sure you have it installed (version 2.8 or later) Clone repository.
git clone git://github.com/zsiciarz/aquila.git aquila-src
Where to build:
a-top-level-directory/ aquila-build/ aquila-src/
In directory you have to build do
cmake ..\aquila-src -DCMAKE_INSTALL_PREFIX=”~/home/mylib” make make install
Installing SFML is simple. You need administrative privilege on the machine
sudo apt-get install libsfml-dev
There is no need to build kiss-fft. You need to add their files while compiling
Compiling and running
Clone the project from here:
git clone firstname.lastname@example.org:indrajithi/Audio-Visualizer.git
For easy compilation use the script
./compile from the project directory
g++ -std=c++11 -c draw.cpp g++ -std=gnu++11 draw.o \ kiss_fft130/kiss_fft.c \ -L /home/<YourUserName>/mylib/lib/ \ -lAquila -lOoura_fft -lm \ -lglut -lGLEW -lGL -lGLU \ -lfreetype -lsfml-system \ -lsfml-audio ./common/shader_utils.o \ -o draw To run the project: ./draw <audio-input>